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PAHvsDCnoPH.Rmd
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PAHvsDCnoPH.Rmd
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---
title: "PAH vs DC, no PH"
author: "Niamh Errington"
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
html_document:
code_folding: hide
toc: true
toc_float: true
toc_collapsed: false
---
PAH vs DC (no PH), MiRXES data set
```{r, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
library(kableExtra)
```
```{r, warning=FALSE, message=FALSE}
library(pacman)
p_load("kableExtra","caret","tidyverse","reshape2","e1071","JamesTools","OptimalCutpoints","Boruta","ggplot2","randomForest","ROCR","rpart","party","rpart.plot","partykit","glmnet","xgboost", "missForest", "mlr", "stringr", "readxl", "pROC", "tidytext")
```
```{r, warning=FALSE, message=FALSE}
#Data processing
pheno <- read_csv("../Phenocleaning/phenotoclean.csv") %>% filter(dana %in% c("PH1.1", "PH1.2", "PH1.3", "PH1.4", "PH0_NOPH", "PH0_CTED"))
load("../Phenocleaning/miRData.RData")
all <- left_join(select(pheno, SampleID, dana, ntprobnp, PARTITION), new, by = "SampleID")
all$dana <- dplyr::recode(all$dana, PH1.4 = "target", PH1.1 = "target", PH1.2 = "target", PH1.3 = "target", PH0_NOPH = "Cont", PH0_CTED = "Cont")
all$dana <- as.factor(all$dana)
discovery <- all[all$PARTITION == "TRAINING",] %>% select(-PARTITION, -ntprobnp)
Interim <- all[all$PARTITION == "INTERIM",] %>% select(-PARTITION, -ntprobnp)
Combined <- all[all$PARTITION != "VALIDATION",] %>% select(-PARTITION, -ntprobnp)
Validation <-all[all$PARTITION == "VALIDATION",] %>% select(-PARTITION, -ntprobnp)
```
# Feature selection
## Boruta
Select miRs which appear in at least 99 out of 100 runs of Boruta
```{r}
multi.boruta<- list()
multi<- function(i){
fit.boruta <- Boruta(factor(dana)~., data=discovery[-1], maxRuns = 300, pValue = 0.01)
boruta.df <- data.frame(attStats(fit.boruta))
multi.boruta[[i]] <- rownames(boruta.df[boruta.df$decision =='Confirmed',])
}
repeatX<- c(1:100)
multi.boruta<- lapply(repeatX, multi)
```
```{r}
#See how many times each miRNA appears when boruta is run 100x
multi.boruta<- as.data.frame(table(unlist(multi.boruta)))
multi.boruta
multi.boruta.mirs<- as.character(multi.boruta[which(multi.boruta$Freq>99),1])
m<- paste(as.vector(multi.boruta.mirs, mode = "any"), collapse = "+")
RFm<- paste("dana ~ ",m,sep = "")
Boruta.data<- discovery[,which(colnames(discovery) %in% c("dana",as.character(multi.boruta.mirs)))]
```
```{r}
control<- trainControl(method = 'repeatedcv',
number = 10,
repeats = 10, classProbs = TRUE, savePredictions = TRUE)
metric <- "Accuracy"
model_weights<- ifelse(Boruta.data$dana == names(table(Boruta.data$dana)[1]), (1/table(Boruta.data$dana)[1])* 0.5, (1/table(Boruta.data$dana)[2])* 0.5)
set.seed(100)
tunegrid <- expand.grid(.predFixed=sqrt(ncol(Boruta.data)), .minNode = 2)
modellist<- list()
for (ntree in c(1000, 1500, 2000, 2500)) {
set.seed(100)
fit <- caret::train(as.formula(RFm), data=Boruta.data, method="Rborist", metric=metric, tuneGrid=tunegrid, trControl=control, ntree=ntree, weight = model_weights)
key <- toString(ntree)
modellist[[key]] <- fit
}
# compare results
results <- resamples(modellist)
summary(results)
res<- summary(results)
res<- as.data.frame(res$statistics$Accuracy)
kable(res)
```
```{r}
ntree = as.numeric(rownames(res)[which(res$Mean == max(res$Mean))])[1]
set.seed(100)
tunegrid <- expand.grid(.predFixed=c(1:15), .minNode = 2)
modellist<- list()
set.seed(100)
fit <- caret::train(as.formula(RFm), data=Boruta.data, method="Rborist", metric=metric, tuneGrid=tunegrid, trControl=control, ntree=ntree, weights = model_weights)
```
```{r}
RFvars<-varImp(fit)[[1]]%>% filter(Overall > 0) %>% rownames_to_column() %>% arrange(desc(Overall)) %>% select(miR = rowname, RFIMP = Overall)
Boruta.short.data<- discovery[,which(colnames(discovery) %in% c("dana", head(RFvars$miR, n =10)))]
mshort<- paste(as.vector(head(RFvars$miR, n =10), mode = "any"), collapse = "+")
RFmShort<- paste("dana ~ ",mshort,sep = "")
```
```{r}
control<- trainControl(method = 'repeatedcv',
number = 10,
repeats = 10, classProbs = TRUE, savePredictions = TRUE)
metric <- "Accuracy"
model_weights<- ifelse(Boruta.short.data$dana == names(table(Boruta.short.data$dana)[1]), (1/table(Boruta.short.data$dana)[1])* 0.5, (1/table(Boruta.short.data$dana)[2])* 0.5)
set.seed(100)
tunegrid <- expand.grid(.predFixed=sqrt(ncol(Boruta.short.data)), .minNode = 2)
modellist<- list()
for (ntree in c(1000, 1500, 2000, 2500)) {
set.seed(100)
fit <- caret::train(as.formula(RFmShort), data=Boruta.short.data, method="Rborist", metric=metric, tuneGrid=tunegrid, trControl=control, ntree=ntree, weight = model_weights)
key <- toString(ntree)
modellist[[key]] <- fit
}
# compare results
resultsshort <- resamples(modellist)
summary(resultsshort)
resshort<- summary(resultsshort)
resshort<- as.data.frame(resshort$statistics$Accuracy)
kable(resshort)
```
```{r}
ntree = as.numeric(rownames(resshort)[which(resshort$Mean == max(resshort$Mean))])[1]
set.seed(100)
tunegrid <- expand.grid(.predFixed=c(1:7), .minNode = 2)
modellist<- list()
set.seed(100)
fit <- caret::train(as.formula(RFmShort), data=Boruta.short.data, method="Rborist", metric=metric, tuneGrid=tunegrid, trControl=control, ntree=ntree, weights = model_weights)
```
### Confusion Matrix on training set
```{r}
tunegrid <- expand.grid(.predFixed=fit$bestTune$predFixed, .minNode = 2)
fit.Boruta <- caret::train(as.formula(RFmShort), data=Boruta.short.data, method="Rborist", metric=metric, tuneGrid=tunegrid, trControl=control, ntree=ntree, weights = model_weights)
Boruta.trained.preds <- predict(fit.Boruta, Boruta.short.data, type = "raw")
confusionMatrix(as.factor(Boruta.trained.preds), discovery$dana, positive = "target")
```
### Confusion Matrix on Interim set
```{r}
Interim.boruta<- Interim[,c(colnames(Interim)%in% colnames(Boruta.short.data))]
#Interim.boruta.short<- Interim.boruta[complete.cases(Interim.boruta),]
Boruta.preds <- predict(fit.Boruta, Interim.boruta[-1], type = "raw")
BorutaInterim<- confusionMatrix(as.factor(Boruta.preds), Interim.boruta$dana, positive = "target")
BorutaInterim
```
## Rpart
The `rpart` function from the [`rpart`](https://cran.r-project.org/web/packages/rpart/rpart.pdf) package can be utilised to grow a regression tree. This tree is built by splitting the data on the single variable which best splits the group in 2. Once the data is separated, this process is applied to each sub-group separately recursively until the subgroups reach a minimum size (defined as 3 below), or no improvement can be made.
Rpart uses a variable selection algorithm called recursive feature elimination (REF, also known as backward selection). Filtering before using Rpart has been shown to improve performance [(Medina et al., 2018)](https://books.google.co.uk/books?id=cdldDwAAQBAJ&pg=PA512&lpg=PA512&dq=filtering+step+before+rpart&source=bl&ots=nmHOKS6mND&sig=ACfU3U35RdZeW4Qa9EyzmmYO-D-_MDyQNw&hl=en&sa=X&ved=2ahUKEwjlsfHf0LjgAhXKQxUIHfnGAu8Q6AEwE3oECAsQAQ#v=onepage&q=filtering%20step%20before%20rpart&f=false)
Method:
1. Fit model with all independent variables.
2. Calculate each variable's importance
3. Use importance to rank all independent variables
4. Remove the variable with the worst rank and build model using the remaining variables
5. Recalculate model accuracy.
6. Repeat steps 4 and 5 until all variables are used.
7. Rank variables based on when they were dropped.
8. Cross-validate to select the best tree. Rpart by default conducts as many splits as possible, and cross-validation is used to prune the tree.
```{r}
#Run rpart in caret
#train control
tc <- trainControl(method="repeatedcv", number=10, repeats = 10, classProbs=TRUE, summaryFunction = twoClassSummary, savePredictions = TRUE)
#minsplit - min no of observations that must exist in a node in order for a split to be attempted
#minbucket - min no of observations in any terminal node
set.seed(100)
fit.caret.rpart <- caret::train(dana ~ ., data=discovery[-1], method='rpart', metric="ROC", trControl=tc, control=rpart.control(minsplit=4, minbucket=5), parms = list(split='gini'), weights = model_weights, maxcompete =0)
#Get predicted resultsfrom Rpart
rpart.pred <- predict(fit.caret.rpart, discovery, type="prob")
#Get ROC curve and AUC
pred.rpart <- ROCR::prediction(predict(fit.caret.rpart, discovery, type="prob")[,2], discovery$dana)
#AUC
#auc.rpart <- performance(pred.rpart, "auc")@y.values[[1]]
#plot(performance(pred.rpart, 'tpr', 'fpr'), main=paste("A from HV regression tree. AUC (of train set):", round(auc.rpart, 2)))
ROCR::performance(pred.rpart, 'tpr', 'fpr')
#Fix names of miRs
fit.caret.rpart$finalModel$frame$var <- gsub("_",'-', fit.caret.rpart$finalModel$frame$var)
rpartmirs<- varImp(fit.caret.rpart)[[1]] %>% filter(Overall > 0) %>% rownames(.)
fit.caret.rpart <- caret::train(dana ~ ., data=select(discovery, "dana", all_of(rpartmirs)), method='rpart', metric="ROC", trControl=tc, control=rpart.control(minsplit=4, minbucket=5), parms = list(split='gini'), weights = model_weights, maxcompete =0)
```
```{r}
prp(fit.caret.rpart$finalModel, main="Cluster A or B Rpart model", extra=2, varlen=0)
plot(as.party(fit.caret.rpart$finalModel), main="Cluster A or B final Rpart model", drop_terminal=F)
rpartmirs<- fit.caret.rpart$coefnames
```
### Confusion Matrix on training set
```{r}
predrpart <- predict(fit.caret.rpart, select(discovery, -dana))
confusionMatrix(as.factor(predrpart), discovery$dana, positive = "target")
```
### Confusion Matrix on Interim set
```{r}
rpart.Interim<- Interim[,colnames(Interim)%in% c("dana", rpartmirs)]
pred.rpart <- predict(fit.caret.rpart, select(rpart.Interim, -dana))
RpartInterim<- confusionMatrix(as.factor(pred.rpart), rpart.Interim$dana, positive = "target")
RpartInterim
```
## LASSO
LASSO (Least Absolute Shrinkage and Selection Operator) is a feature selection method designed to reduce over-fitting. It automatically selects the significant variables by shrinking the coefficients of predictors deemed unimportant to zero. A simple explanation of LASSO can be found [here](http://statweb.stanford.edu/~tibs/lasso/simple.html).
`Glmnet` ([Vignette](http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html)) is a package that uses penalised maximum likelihood to fit a generalised linear model.
In the plot above 'each curve corresponds to a variable. It shows the path of its coefficient against the L1-norm of the whole coefficient vector at as $\lambda$ varies. The axis above indicates the number of non-zero coefficients at the current $\lambda$, which is the effective degrees of freedom (df) for the lasso.'
As such, it isn't possible to deduce which of the coefficients are 'best predictors' from the graph above, and some sort of cross-validation is necessary:
`nfolds` parameter can be as large as the sample size (leave one out CV), but this is not recommended for large datasets.
```{r}
fit.glmcv <- cv.glmnet(x=as.matrix(select(discovery,-dana, -SampleID)), y=as.factor(discovery$dana), alpha=1, family='binomial', nfolds=10, type.measure = "deviance", weights = model_weights, dfmax = 12)
summary(fit.glmcv)
other.glmcv <- cv.glmnet(x=as.matrix(select(discovery,-dana, -SampleID)), y=as.factor(discovery$dana), alpha=1, family='binomial', nfolds=10, type.measure = "class", weights = model_weights, dfmax = 12)
model.lambda <- fit.glmcv$lambda.min
plot(fit.glmcv, cex.axis=1.5, cex.lab=1.5,cex.main=1.5)
plot(other.glmcv, cex.axis=1, cex.lab=1, cex.main=1)
```
In the binomial deviance plot above, the red indicates the cross validation curve, and the the error bars show upper and lower standard deviation curves.
The two dotted lines show `lambda.min` - the value of $\lambda$ that gives minimum mean cross-validated error. The other $\lambda$ is `lambda.1se`, which gives the most regularised model such that the error is within 1 standard error of the minimum.
```{r}
#refit model for new lambda
fit.glm.new <- glmnet(x=as.matrix(select(discovery,-dana, -SampleID)), y=as.factor(discovery$dana), alpha=1, family='binomial', weights = model_weights, lambda = fit.glmcv$lambda, dfmax = 12)
lasso.model<- fit.glm.new %>% coef() %>% as.matrix() %>% as.data.frame() %>% rownames_to_column %>% filter(abs(s0) >0)
m.LASSO <- cbind("dana" = discovery$dana, discovery[,colnames(discovery) %in% lasso.model$rowname])
control<- trainControl(method = 'repeatedcv',
number = 10,
repeats = 10,
savePredictions = TRUE,
classProbs = TRUE)
tuneLASSO.min<- expand.grid(.alpha = 1, .lambda = fit.glmcv$lambda.min)
LASSO.min<- caret::train(dana ~ ., data = select(discovery,-SampleID), method = "glmnet", trControl = control, family = 'binomial', tuneGrid = tuneLASSO.min, weights = model_weights)
lasso.model.min<- coef(LASSO.min$finalModel, LASSO.min$bestTune$lambda) %>% as.matrix() %>% as.data.frame() %>% rownames_to_column %>% filter(abs(`1`) >0)
LASSO.min.data<- discovery[,colnames(discovery) %in% c("dana", lasso.model.min$rowname[-1])]
LASSO.min<- caret::train(dana ~ ., data = LASSO.min.data, method = "glmnet", trControl = control, family = 'binomial', tuneGrid = tuneLASSO.min, weights = model_weights)
tuneLASSO.1se<- expand.grid(.alpha = 1, .lambda = fit.glmcv$lambda.1se)
LASSO.1se<- caret::train(dana ~ ., data = select(discovery, -SampleID), method = "glmnet", trControl = control, family = 'binomial', tuneGrid = tuneLASSO.1se, weights = model_weights)
lasso.model.1se<- coef(LASSO.1se$finalModel, LASSO.1se$bestTune$lambda) %>% as.matrix() %>% as.data.frame() %>% rownames_to_column %>% filter(abs(`1`) >0)
LASSO.1se.data<- discovery[,colnames(discovery) %in% c("dana", lasso.model.1se$rowname[-1])]
LASSO.1se<- caret::train(dana ~ ., data = LASSO.1se.data, method = "glmnet", trControl = control, family = 'binomial', tuneGrid = tuneLASSO.1se, weights = model_weights)
kable(lasso.model.min, caption = paste("miRs retained by LASSO lambda.min: (",length(lasso.model.min $rowname),")")) %>% kable_styling(full_width = TRUE)
kable(lasso.model.1se, caption = paste("miRs retained by LASSO lambda.1se: (",length(lasso.model.1se$rowname),")")) %>% kable_styling(full_width = TRUE)
```
### Confusion Matrix on training set
```{r, eval = FALSE}
probabilities<- predict(fit.glm.new, newx=as.matrix(select(discovery,-dana)),type = "response")
predicted.classes <- ifelse(probabilities > 0.5, "Cont", "target")
predicted.classes <- as.factor(predicted.classes)
confusionMatrix(predicted.classes,discovery$dana, positive = "target")
```
### Confusion Matrix on interim set
```{r}
LASSO.pred.min <- predict(LASSO.min, newdata=select(Interim, colnames(LASSO.min.data),-dana))
LASSOminInterim<- confusionMatrix(LASSO.pred.min,Interim$dana, positive = "target")
LASSOminInterim
LASSO.pred.1se<- predict(LASSO.1se, newdata=select(Interim, colnames(LASSO.1se.data),-dana))
LASSO1seInterim<- confusionMatrix(LASSO.pred.1se,Interim$dana, positive = "target")
LASSO1seInterim
```
## XGBoost
[XGBoost](https://cran.r-project.org/web/packages/xgboost/xgboost.pdf) (**Ex**treme **G**radient **B**oosting) is an optimised distributed gradient boosting library that performs better than gradient boosting (GBM) framework alone.
Simple explanation with tutorial be found [here](https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/beginners-tutorial-on-xgboost-parameter-tuning-r/tutorial/) and [here](https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/beginners-tutorial-on-xgboost-parameter-tuning-r/tutorial/).
### Parameters for tuning
`nrounds` - maximum number of iterations (similar to no. of trees grown).
`eta` - [range: (0,1)] - learning rate. After every round, it shrinks the feature weights to reach the best optimum. Lower eta = slower computation
`gamma` [range: $(0,\infty)$] - controls regularisation (prevents over-fitting)
`max_depth` [range: $(0,\infty)$] - controls the tree depth. The larger the depth, the more complex the model and the higher the chances of over-fitting. Larger data sets require deep trees
`min_child_weight` [range: $(0,\infty)$] - In classification, if the leaf node has a minimum sum of instance weight lower than min_child_weight, the tree splitting stops. I.e. it stops potential future interactions to reduce over-fitting
`subsample` [range: (0,1)] - Controls the number of samples supplied to a tree
`colsample_bytree` [range: (0,1)] - controls the no. of features (variables) supplied to a tree
| Parameter | Default |Range | Values attempted | Final model |
|--------------------|---------|------------|---------------------------------|-------------|
|`nrounds` | 100 | | 100 - 10 000 | 3950 |
|`eta` | 0.3 |(0,1) |0.01, 0.025, 0.05, 0.1, 0.2, 0.3 | 0.1 |
|`gamma` | 0 | $(0,\infty)$ | 0,0.05, 0.1, 0.5, 0.7, 0.9, 1 | 0 |
| `max_depth` | 6 | $(0,\infty)$ | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 | 9 |
|`colsample_bytree` | 1 | (0,1) | 0.4, 0.6, 0.8, 1.0 | 1 |
|`subsample` | 1 | (0,1) |0.5, 0.75, 1.0 | 1 |
|`min_child_weight` | 1 | $(0,\infty)$ | 1, 2, 3, 4, 5, 6 | 3 |
`scale_pos_weight` = 746/257
```{r}
train<- data.matrix(select(discovery, -dana, -SampleID))
labels<- discovery$dana
#Set up XGBoost with default hyperparameters
grid_default <- expand.grid(
nrounds = 100,
max_depth = 6,
eta = 0.3,
gamma = 0,
colsample_bytree = 1,
min_child_weight = 1,
subsample = 1
)
train_control <- caret::trainControl(
method = "none",
verboseIter = FALSE, # no training log
allowParallel = TRUE # FALSE for reproducible results
)
xgb_base<- caret::train(
x = train,
y = as.factor(labels),
trControl = train_control,
tuneGrid = grid_default,
method = "xgbTree",
verbose = TRUE,
scale_pos_weight = table(labels)[[1]]/table(labels)[[2]]
)
nrounds<- seq(from = 100, to =1000, by = 50)
#tune using caret
tune_grid <- expand.grid(
#nrounds = seq(from = 100, to = 1000, by = 20),
nrounds = nrounds,
eta = c(0.025, 0.05, 0.1, 0.2, 0.3),
max_depth = c(1, 2, 3, 4, 5, 6),
gamma = 0,
colsample_bytree = 1,
min_child_weight = 1,
subsample = 1
)
set.seed(25)
tune_control <- caret::trainControl(
method = "cv", # cross-validation
number = 10, # with n folds
repeats = 10, #the no. of complete sets of folds to compute
#index = createFolds(tr_treated$Id_clean), # fix the folds
verboseIter = FALSE, # no training log
allowParallel = TRUE # FALSE for reproducible results
)
set.seed(25)
xgb_tune <- caret::train(
x = train,
y = as.factor(labels),
trControl = tune_control,
tuneGrid = tune_grid,
method = "xgbTree",
verbose = TRUE,
scale_pos_weight = table(labels)[[1]]/table(labels)[[2]]
)
#function to help plot
tuneplot <- function(x, probs = .90) {
ggplot(x) +
coord_cartesian(ylim = c(quantile(x$results$Accuracy, probs = probs), min(x$results$Accuracy))) +
theme_bw()
}
kable(xgb_tune$bestTune, caption = "1st tuning, best parameters")%>% kable_styling(full_width = TRUE)
```
```{r}
tune_grid2 <- expand.grid(
nrounds = nrounds,
eta = xgb_tune$bestTune$eta,
max_depth = seq(from = 1, to =10, by = 1),
gamma = 0,
colsample_bytree = 1,
min_child_weight = c(1,2,3,4,5,6),
subsample = 1
)
set.seed(25)
xgb_tune2 <- caret::train(
x = train,
y = as.factor(labels),
trControl = tune_control,
tuneGrid = tune_grid2,
method = "xgbTree",
verbose = TRUE,
scale_pos_weight = table(labels)[[1]]/table(labels)[[2]]
)
kable(xgb_tune2$bestTune, caption = "2nd tuning, best parameters")%>% kable_styling(full_width = TRUE)
```
```{r}
tune_grid3 <- expand.grid(
nrounds = nrounds,
eta = xgb_tune$bestTune$eta,
max_depth = xgb_tune2$bestTune$max_depth,
gamma = 0,
colsample_bytree = c(0.4, 0.6, 0.8, 1.0),
min_child_weight = xgb_tune2$bestTune$min_child_weight,
subsample = c(0.5, 0.75, 1.0)
)
set.seed(25)
xgb_tune3 <- caret::train(
x = train,
y = as.factor(labels),
trControl = tune_control,
tuneGrid = tune_grid3,
method = "xgbTree",
verbose = TRUE,
scale_pos_weight = table(labels)[[1]]/table(labels)[[2]]
)
kable(xgb_tune3$bestTune, caption = "3rd tuning, best parameters")%>% kable_styling(full_width = TRUE)
```
```{r}
tune_grid4 <- expand.grid(
nrounds = nrounds,
eta = xgb_tune$bestTune$eta,
max_depth = xgb_tune3$bestTune$max_depth,
gamma = c(0,0.05, 0.1, 0.5, 0.7, 0.9, 1),
colsample_bytree = xgb_tune3$bestTune$colsample_bytree,
min_child_weight = xgb_tune2$bestTune$min_child_weight,
subsample = xgb_tune3$bestTune$subsample
)
set.seed(25)
xgb_tune4 <- caret::train(
x = train,
y = as.factor(labels),
trControl = tune_control,
tuneGrid = tune_grid4,
method = "xgbTree",
verbose = TRUE,
scale_pos_weight = table(labels)[[1]]/table(labels)[[2]]
)
kable(xgb_tune4$bestTune, caption = "4th tuning, best parameters")%>% kable_styling(full_width = TRUE)
```
```{r}
tune_grid5 <- expand.grid(
nrounds = seq(from = 100, to = 10000, by = 50),
eta = c(0.01,0.025,0.05,0.1,0.2,0.3),
max_depth = xgb_tune3$bestTune$max_depth,
gamma = xgb_tune4$bestTune$gamma,
colsample_bytree = xgb_tune3$bestTune$colsample_bytree,
min_child_weight = xgb_tune2$bestTune$min_child_weight,
subsample = xgb_tune3$bestTune$subsample
)
set.seed(25)
xgb_tune5 <- caret::train(
x = train,
y = as.factor(labels),
trControl = tune_control,
tuneGrid = tune_grid5,
method = "xgbTree",
verbose = TRUE,
scale_pos_weight = table(labels)[[1]]/table(labels)[[2]]
)
```
```{r}
final_grid <- expand.grid(
nrounds = xgb_tune5$bestTune$nrounds,
eta = xgb_tune5$bestTune$eta,
max_depth = xgb_tune5$bestTune$max_depth,
gamma = xgb_tune5$bestTune$gamma,
colsample_bytree = xgb_tune5$bestTune$colsample_bytree,
min_child_weight = xgb_tune5$bestTune$min_child_weight,
subsample = xgb_tune5$bestTune$subsample
)
set.seed(25)
xgb_tune_final <- caret::train(
x = train,
y = as.factor(labels),
trControl = tune_control,
tuneGrid = final_grid,
method = "xgbTree",
verbose = TRUE,
scale_pos_weight = table(labels)[[1]]/table(labels)[[2]]
)
```
```{r}
kable(xgb_tune_final$bestTune, caption = "Final tuning, best parameters")%>% kable_styling(full_width = TRUE)
```
```{r}
xgb_imp<- varImp(xgb_tune_final, scale = FALSE)
plot(xgb_imp)
```
### Confusion Matrix on training set
```{r}
xgbpredict<- predict(xgb_tune_final, train)
confusionMatrix(xgbpredict, as.factor(labels), positive = "target")
```
### Confusion Matrix on Interim set
```{r}
test<- Interim[,colnames(Interim) %in% colnames(discovery)]
testlabels<- test$dana
test<- data.matrix(select(test,-dana))
xgbpredict.testset<- predict(xgb_tune_final, test)
confusionMatrix(xgbpredict.testset, as.factor(testlabels), positive = "target")
```
## XGBoost on selected miRs
```{r}
xgbmir<- head(rownames(xgb_imp$importance), n=10)
xgb2<- select(discovery, c("dana", xgbmir))
labels2<- xgb2$dana
xgb2<- data.matrix(select(xgb2, -dana))
#Set up XGBoost with default hyperparameters
grid_default <- expand.grid(
nrounds = 100,
max_depth = 6,
eta = 0.3,
gamma = 0,
colsample_bytree = 1,
min_child_weight = 1,
subsample = 1
)
train_control <- caret::trainControl(
method = "none",
verboseIter = FALSE, # no training log
allowParallel = TRUE # FALSE for reproducible results
)
xgb_base_s<- caret::train(
x = xgb2,
y = as.factor(labels2),
trControl = train_control,
tuneGrid = grid_default,
method = "xgbTree",
verbose = TRUE,
scale_pos_weight = table(labels)[[1]]/table(labels)[[2]]
)
nrounds<- seq(from = 100, to =1000, by = 50)
#tune using caret
tune_grid <- expand.grid(
#nrounds = seq(from = 100, to = 1000, by = 20),
nrounds = nrounds,
eta = c(0.025, 0.05, 0.1, 0.2, 0.3),
max_depth = c(1, 2, 3, 4, 5, 6),
gamma = 0,
colsample_bytree = 1,
min_child_weight = 1,
subsample = 1
)
set.seed(25)
tune_control <- caret::trainControl(
method = "repeatedcv", # cross-validation
number = 10, # with n folds
repeats = 10, #the no. of complete sets of folds to compute
#index = createFolds(tr_treated$Id_clean), # fix the folds
verboseIter = FALSE, # no training log
allowParallel = TRUE # FALSE for reproducible results
)
set.seed(25)
xgb_tune_s <- caret::train(
x = xgb2,
y = as.factor(labels2),
trControl = tune_control,
tuneGrid = tune_grid,
method = "xgbTree",
verbose = TRUE,
scale_pos_weight = (table(labels)[[1]]/table(labels)[[2]])
)
```
```{r}
tune_grid2s <- expand.grid(
nrounds = nrounds,
eta = xgb_tune_s$bestTune$eta,
max_depth = seq(from = 1, to =10, by = 1),
gamma = 0,
colsample_bytree = 1,
min_child_weight = c(1,2,3,4,5,6),
subsample = 1
)
set.seed(25)
xgb_tune2s <- caret::train(
x = xgb2,
y = as.factor(labels2),
trControl = tune_control,
tuneGrid = tune_grid2s,
method = "xgbTree",
verbose = TRUE,
scale_pos_weight = table(labels)[[1]]/table(labels)[[2]]
)
kable(xgb_tune2s$bestTune, caption = "2nd tuning, best parameters")%>% kable_styling(full_width = TRUE)
```
```{r}
tune_grid3s <- expand.grid(
nrounds = nrounds,
eta = xgb_tune_s$bestTune$eta,
max_depth = xgb_tune2s$bestTune$max_depth,
gamma = 0,
colsample_bytree = c(0.4, 0.6, 0.8, 1.0),
min_child_weight = xgb_tune2s$bestTune$min_child_weight,
subsample = c(0.5, 0.75, 1.0)
)
set.seed(25)
xgb_tune3s <- caret::train(
x = xgb2,
y = as.factor(labels2),
trControl = tune_control,
tuneGrid = tune_grid3s,
method = "xgbTree",
verbose = TRUE,
scale_pos_weight = table(labels)[[1]]/table(labels)[[2]]
)
kable(xgb_tune3s$bestTune, caption = "3rd tuning, best parameters")%>% kable_styling(full_width = TRUE)
```
```{r}
tune_grid4s <- expand.grid(
nrounds = nrounds,
eta = xgb_tune_s$bestTune$eta,
max_depth = xgb_tune3s$bestTune$max_depth,
gamma = c(0,0.05, 0.1, 0.5, 0.7, 0.9, 1),
colsample_bytree = xgb_tune3s$bestTune$colsample_bytree,
min_child_weight = xgb_tune2s$bestTune$min_child_weight,
subsample = xgb_tune3s$bestTune$subsample
)
set.seed(25)
xgb_tune4s <- caret::train(
x = xgb2,
y = as.factor(labels2),
trControl = tune_control,
tuneGrid = tune_grid4s,
method = "xgbTree",
verbose = TRUE,
scale_pos_weight = table(labels)[[1]]/table(labels)[[2]]
)
kable(xgb_tune4s$bestTune, caption = "4th tuning, best parameters")%>% kable_styling(full_width = TRUE)
```
```{r}
tune_grid5s <- expand.grid(
nrounds = seq(from = 100, to = 10000, by = 50),
eta = c(0.01,0.025,0.05,0.1,0.2,0.3),
max_depth = xgb_tune3s$bestTune$max_depth,
gamma = xgb_tune4s$bestTune$gamma,
colsample_bytree = xgb_tune3s$bestTune$colsample_bytree,
min_child_weight = xgb_tune2s$bestTune$min_child_weight,
subsample = xgb_tune3s$bestTune$subsample
)
set.seed(25)
xgb_tune5s <- caret::train(
x = xgb2,
y = as.factor(labels2),
trControl = tune_control,
tuneGrid = tune_grid5s,
method = "xgbTree",
verbose = TRUE,
scale_pos_weight = table(labels)[[1]]/table(labels)[[2]]
)
```
```{r}
final_grid_s <- expand.grid(
nrounds = xgb_tune5s$bestTune$nrounds,
eta = xgb_tune5s$bestTune$eta,
max_depth = xgb_tune5s$bestTune$max_depth,
gamma = xgb_tune5s$bestTune$gamma,
colsample_bytree = xgb_tune5s$bestTune$colsample_bytree,
min_child_weight = xgb_tune5s$bestTune$min_child_weight,
subsample = xgb_tune5s$bestTune$subsample
)
set.seed(25)
tune_control <- caret::trainControl(
method = "repeatedcv", # cross-validation
number = 10, # with n folds
repeats = 10, #the no. of complete sets of folds to compute
#index = createFolds(tr_treated$Id_clean), # fix the folds
verboseIter = FALSE, # no training log
allowParallel = TRUE, # FALSE for reproducible results
savePredictions = TRUE,
classProbs = TRUE
)
set.seed(25)
xgb_tune_final_s <- caret::train(
x = xgb2,
y = as.factor(labels2),
trControl = tune_control,
tuneGrid = final_grid_s,
method = "xgbTree",
verbose = TRUE,
scale_pos_weight = table(labels)[[1]]/table(labels)[[2]]
)
```
```{r}
kable(xgb_tune_final_s$bestTune, caption = "Final tuning, best parameters")%>% kable_styling(full_width = TRUE)
```
```{r}
test.short<- test[,colnames(xgb2)]
xgbpredict.testset.s<- predict(xgb_tune_final_s, test.short)
XGBInterim<- confusionMatrix(xgbpredict.testset.s, as.factor(testlabels), positive = "target")
XGBInterim
```
# Overlapping miRs
```{r}
#get list of any mir appearing in a feature selection method
allmirs<- unique(c(lasso.model$rowname[-1], rownames(multi.boruta.mirs), rpartmirs, xgbmir))
#get list of mirs appearing in at least 2 feature selection methods
dupmirs<- unique(c(lasso.model$rowname, rownames(multi.boruta.mirs), rpartmirs, xgbmir)[duplicated(c(lasso.model$rowname, rownames(multi.boruta.mirs), rpartmirs, xgbmir))])
```
```{r}
#pick mirs selected by any of the 3 methods
selectedmirs <- c("dana",dupmirs)
#reduce ml.data2 to contain only mirs in selectedmirs
selecmirs<- discovery[,which(colnames(discovery) %in% selectedmirs)]
#melt dataframe so ggplot can be used
df<- melt(selecmirs, id.var = "dana") %>% mutate (dana = factor(dana, ))
ggplot(data = df, aes(x= variable, y=value)) + geom_boxplot(aes(fill=dana)) + theme(text = element_text(size=12), axis.text.x=element_text(angle=90, vjust=0.5, size = 12),axis.text.y=element_text(size = 12)) + labs(x="", y="Expression", size = 12)
```
# CV ROCs
## Boruta
```{r, message=FALSE, warning = FALSE}
boruta.split<- split(fit.Boruta$pred, fit.Boruta$pred$Resample)
lapply(repeatX, function(d) {
pROC::auc(pROC::roc(predictor = boruta.split[[d]]$target, response = boruta.split[[d]]$obs))[1]
}) %>% unlist() %>% hist(main = paste("Boruta CV mean AUC:", round(mean(.),2), "(SD:", round(sd(.),2), ")"))
```
## Rpart
```{r, warning=FALSE, message=FALSE}
rpart.auc<- list()
rpart.split<- split(fit.caret.rpart$pred, fit.caret.rpart$pred$Resample)
lapply(repeatX, function(d) {
pROC::auc(pROC::roc(predictor = rpart.split[[d]]$target, response = rpart.split[[d]]$obs))[1]
}) %>% unlist() %>% hist(main = paste("Rpart CV mean AUC:", round(mean(.),2), "(SD:", round(sd(.),2), ")"))
```
## LASSO (caret model - lambda min)
```{r, warning = FALSE, message=FALSE}
LASSO.split.min<- split(LASSO.min$pred, LASSO.min$pred$Resample)
lapply(repeatX, function(d) {
pROC::auc(pROC::roc(predictor = LASSO.split.min[[d]]$target, response = LASSO.split.min[[d]]$obs))[1]
}) %>% unlist() %>% hist(main = paste("LASSO CV mean AUC:", round(mean(.),2), "(SD:", round(sd(.),2), ")"))
```
## LASSO (caret model - lambda 1se)
```{r, warning = FALSE, message=FALSE}
LASSO.split.1se<- split(LASSO.1se$pred, LASSO.1se$pred$Resample)
lapply(repeatX, function(d) {
pROC::auc(pROC::roc(predictor = LASSO.split.1se[[d]]$target, response = LASSO.split.1se[[d]]$obs))[1]
}) %>% unlist() %>% hist(main = paste("LASSO CV mean AUC:", round(mean(.),2), "(SD:", round(sd(.),2), ")"))
```
## XGBoost
```{r, warning = FALSE, message=FALSE}
tosplit<- xgb_tune_final_s
XGB.split<- split(tosplit$pred, tosplit$pred$Resample)
parallel::mclapply(repeatX, function(d) {
pROC::auc(pROC::roc(predictor = XGB.split[[d]]$target, response = XGB.split[[d]]$obs))[1]
}) %>% unlist() %>% hist(main = paste("XGBoost CV mean AUC:", round(mean(.),2), "(SD:", round(sd(.),2), ")"))
```
# ROC on interim set
## Boruta
```{r}
Boruta.perf<- predict(fit, select(Interim.boruta, -dana), type= "prob")
Boruta.auc<- pROC::auc(predictor = Boruta.perf$target, response = Interim.boruta$dana)
plot(pROC::roc(predictor = Boruta.perf$target, response = Interim.boruta$dana), main = paste("AUC:", round(Boruta.auc,2)))
```
## Rpart
```{r}
rpart.perf<- predict(fit.caret.rpart, select(rpart.Interim, -dana), type= "prob")
rpart.auc<- pROC::auc(predictor = rpart.perf$target, response = rpart.Interim$dana)
plot(pROC::roc(predictor = rpart.perf$target, response = rpart.Interim$dana), main = paste("AUC:", round(rpart.auc,2)))
```
## LASSO lambda min
```{r}
LASSO.probs.min <- predict(LASSO.min, newdata=select(Interim, colnames(LASSO.min.data),-dana), type = "prob")
LASSO.auc.min<- pROC::auc(predictor = LASSO.probs.min$target, response = Interim$dana)
plot(pROC::roc(predictor = LASSO.probs.min$target, response = Interim$dana), main = paste("AUC:", round(LASSO.auc.min,2)))
```
## LASSO lambda 1se
```{r}
LASSO.probs.1se <- predict(LASSO.1se, newdata=select(Interim, colnames(LASSO.1se.data),-dana), type = "prob")
LASSO.auc.1se<- pROC::auc(predictor = LASSO.probs.1se$target, response = Interim$dana)
plot(pROC::roc(predictor = LASSO.probs.1se$target, response = Interim$dana), main = paste("AUC:", round(LASSO.auc.1se,2)))
```
## XGBoost
```{r}
xgbpreds<- predict(xgb_tune_final_s, test.short, type = "prob")
xgb.auc<- pROC::auc(predictor = xgbpreds$target, response = testlabels)
plot(pROC::roc(predictor = xgbpreds$target, response = testlabels), main = paste("AUC:", round(xgb.auc,2)))
```
# Selected miRs & Variable Importance
```{r}
kable(table(discovery$dana), caption = "Groups in training set")
kable(table(Interim$dana), caption = "Groups in interim set")
```
```{r, message=FALSE}
RFvars<-varImp(fit.Boruta)[[1]]%>% filter(Overall > 0) %>% rownames_to_column() %>% arrange(desc(Overall)) %>% select(miR = rowname, RFIMP = Overall)
rpartvars<- varImp(fit.caret.rpart)[[1]] %>% filter(Overall > 0) %>% rownames_to_column() %>% arrange(desc(Overall)) %>% select(miR = rowname, rpartIMP = Overall)
LASSOminvars<- varImp(LASSO.min)[[1]] %>% filter(Overall > 0)%>% rownames_to_column() %>% arrange(desc(Overall)) %>% select(miR = rowname, LASSOminIMP = Overall)
LASSO1sevars<- varImp(LASSO.1se)[[1]] %>% filter(Overall > 0) %>% rownames_to_column() %>% arrange(desc(Overall)) %>% select(miR = rowname, LASSO1seIMP = Overall)
XGBvars<- varImp(xgb_tune_final_s)[[1]]%>% filter(Overall > 0) %>% rownames_to_column() %>% arrange(desc(Overall)) %>% select(miR = rowname, XGBIMP = Overall)
allimps <- dplyr::full_join(x = RFvars, y= rpartvars) %>% full_join(x=., y=LASSOminvars) %>% full_join(x=., y=LASSO1sevars) %>% full_join(x=., XGBvars)
kable(RFvars, caption = paste("RF miRNAs (", length(RFvars$miR), ")"))
rpart.vars<-labels(fit.caret.rpart$finalModel)[-1]
rpart.vars<- gsub("<.*","", rpart.vars)
rpart.vars<- unique(gsub(">.*","", rpart.vars))
kable(rpartvars, caption = paste("Rpart miRs: (",length(rpartvars$miR),")"))
kable(LASSOminvars, caption = paste("LASSO min miRs: (", length(LASSOminvars$miR),")"))
kable(LASSO1sevars, caption = paste("LASSO 1se miRs: (", length(LASSO1sevars$miR),")"))
kable(XGBvars, caption = paste("XGBoost miRs: (",length(XGBvars$miR),")"))
```
# Discovery + Interim
```{r}
#run boruta
multi.boruta.DI<- list()
multi<- function(i){
fit.boruta <- Boruta(factor(dana)~., data=select(Combined, -SampleID), maxRuns = 300, pValue = 0.01)
boruta.df <- data.frame(attStats(fit.boruta))
multi.boruta.DI[[i]] <- rownames(boruta.df[boruta.df$decision =='Confirmed',])
}
repeatX<- c(1:100)
multi.boruta.DI<- lapply(repeatX, multi)
#See how many times each miRNA appears when boruta is run 100x
multi.boruta.DI<- as.data.frame(table(unlist(multi.boruta.DI)))
multi.boruta.mirs.DI<- as.character(multi.boruta.DI[which(multi.boruta.DI$Freq>94),1])
multi.boruta.DI
```
```{r}
m2<- paste(as.vector(as.character(multi.boruta.DI$Var1), mode = "any"), collapse = "+")
RFm2<- paste("dana ~ ", m2,sep = "")
Boruta.data.DI<- Combined[,which(colnames(Combined) %in% c("dana",as.character(multi.boruta.DI$Var1)))]
```
```{r}
control<- trainControl(method = 'repeatedcv',
number = 10,
repeats = 10, savePredictions = TRUE, classProbs = TRUE)
metric <- "Accuracy"
model_weights<- ifelse(Boruta.data.DI$dana == names(table(Boruta.data.DI$dana)[1]), (1/table(Boruta.data.DI$dana)[1])* 0.5, (1/table(Boruta.data.DI$dana)[2])* 0.5)
tunegrid <- expand.grid(.predFixed=sqrt(ncol(Boruta.data.DI)), .minNode = 2)
modellist<- list()
for (ntree in c(1000, 1500, 2000, 2500)) {
set.seed(100)
fit.DI <- caret::train(as.formula(RFm2), data=Boruta.data.DI, method="Rborist", metric=metric, tuneGrid=tunegrid, trControl=control, ntree=ntree, weights = model_weights)
key <- toString(ntree)
modellist[[key]] <- fit.DI
}
# compare results
resultsDI <- resamples(modellist)
resDI<- summary(resultsDI)
resDI<- as.data.frame(resDI$statistics$Accuracy)
kable(resDI)%>% kable_styling(full_width = TRUE)
ntree = as.numeric(rownames(resDI)[which(resDI$Mean == max(resDI$Mean))])[1]
set.seed(100)
tunegrid <- expand.grid(.predFixed=c(1:15), .minNode = 2)
modellist<- list()
set.seed(100)